Gang Niu

11

Papers

63

Total Citations

Papers (11)

Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios

Realistic Evaluation of Deep Partial-Label Learning Algorithms

Direct Distillation between Different Domains

Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation

Towards Out-of-Modal Generalization without Instance-level Modal Correspondence

Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning

Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization

Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical

Balancing Similarity and Complementarity for Federated Learning

Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training

Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought